The CAC framework can be viewed as a classification algorithm that, by design, finds clusters that are intended to be used as training datasets by the (base) classifiers of choice e.g., Logistic Regression (LR), SVM or Neural Networks.
The key idea of our approach is to induce class separability (polarisation) within each cluster in addition to dividing the data for better classification.
This is effected by designing a cost function that can be used within the optimization framework of \(k\) -means.
However, finding clusters with this cost function using the popular Lloyd's algorithm may not guarantee convergence.
Therefore, we design an approach based on Hartigan's method [1]} and prove that it converges.
Our design is generic and not classifier-specific, and leads to an efficient iterative method to find the clusters.
We also theoretically investigate the benefits offered by class separation to classifiers employing the standard log-loss function. We find that the upper and lower bounds for the standard log-loss of a dataset are linearly dependent on class separation within the dataset.
We analyze the sensitivity of CAC to various data characteristics such as class separation, intercluster distance, and the number of clusters using synthetic data.
Finally, we empirically study the performance of CAC on benchmark datasets, including a large clinical dataset.
In summary, our contributions are:
